A channel estimator, comprises: a receiver receives a first time-domain training sequence; a first convolution circuit generates an estimated value for the first time-domain training sequence by convoluting a second time-domain training sequence with a current channel estimation value; a first subtractor generates an error by subtracting the estimated value for the first time-domain training sequence from the value of the first time-domain training sequence; an updating circuit generates an updated channel estimation value by updating the current channel estimation value with the error; the receiver iteratively receives a next symbol of the first time-domain training sequence, the first convolution circuit, the subtractor and the updating circuit repeat their operation until completion of receipt of a last symbol of the first time-domain training sequence. The updating circuit outputs the current updated channel estimation value upon completion of receipt of a last first time-domain training sequence.
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1. A channel estimator, comprising: a receiver, configured to receive a symbol of a first time-domain training sequence; a first convolution circuit, configured to generate an estimated value for the first time-domain training sequence by convoluting a second time-domain training sequence with a current channel estimation value, wherein the second time-domain training sequence represents a time-domain training sequence generated by the receiver; a first subtractor coupled to both the receiver and the first convolution circuit, and configured to generate an error by subtracting the estimated value for the first time-domain training sequence from a value of the first time-domain training sequence; an updating circuit coupled to both the first subtractor and the first convolution circuit and configured to generate an updated channel estimation value by updating the current channel estimation value with the error, and sending the updated channel estimation value to the first convolution circuit; wherein the receiver iteratively receives a next symbol of the first time-domain training sequence, the first convolution circuit, the first subtractor and the updating circuit repeat their operation using the updated channel estimation value until completion of receipt of a last symbol of the first time-domain training sequence, and the updating circuit is configured to output the current updated channel estimation value as a channel estimation result upon receipt of a last symbol of the first time-domain training sequence.
A channel estimator receives a time-domain training sequence symbol. It uses a convolution circuit that estimates the training sequence by convolving another time-domain training sequence (generated by the receiver) with the current channel estimate. A subtractor then calculates the error between the estimated and actual training sequence. An updating circuit refines the channel estimate using this error and sends the updated estimate back to the convolution circuit. This process repeats for each symbol of the training sequence. Finally, the estimator outputs the fully updated channel estimate after processing all symbols.
2. The channel estimator of claim 1 , further comprising an equalizer configured to generate a pre-equalized signal by pre-equalizing a received signal; a signal re-constructor coupled to the equalizer and configured to generate a reconstructed interference signal based on the pre-equalized signal; a second subtractor coupled to both the signal re-constructor and the receiver and configured to generate the first time-domain training sequence by subtracting the reconstructed interference signal from a received signal for a frame.
In addition to the channel estimator described previously, this version includes an equalizer that pre-equalizes the received signal. A signal reconstructor generates an estimated interference signal based on the pre-equalized signal. Then, a subtractor creates the primary time-domain training sequence by subtracting this reconstructed interference from the originally received signal for a given frame. This training sequence is then used by the channel estimator.
3. The channel estimator of claim 2 , wherein the equalizer further comprises a FFT circuit, configured to generate a FFT result by performing FFT calculation on the received signal; a divider coupled to the FFT circuit and configured to generate a quotient by dividing the FFT result by a channel estimation value of a previous frame; and a decision circuit, coupled to the divider and configured to retrieve a transmitting signal based on the quotient.
Within the equalizer described above, a Fast Fourier Transform (FFT) circuit converts the received signal to the frequency domain. A divider then divides the FFT result by a channel estimation value obtained from the previous frame. A decision circuit determines the transmitted signal based on the resulting quotient. This processed signal is then used to generate the pre-equalized signal, as described in claim 2.
4. The channel estimator of claim 2 , wherein the signal re-constructor further comprises an IFFT circuit configured to generate an IFFT result by performing IFFT calculation on the pre-equalized signal; and a second convolution circuit, configured to generate the reconstructed interference signal by convoluting the IFFT result with a channel estimation value of a previous frame.
The signal reconstructor, used as part of the larger channel estimator, implements interference reconstruction by using an Inverse Fast Fourier Transform (IFFT) circuit to convert the pre-equalized signal (as described in claim 2) back to the time domain. Then, a convolution circuit generates the reconstructed interference signal by convolving the IFFT result with a channel estimation value from the previous frame.
5. The channel estimator of claim 2 , wherein the signal re-constructor further comprises a multiplier, configured to generate a multiplied signal by multiplying the pre-equalized signal with a channel estimation value of a previous frame; and an IFFT circuit configured to generate the reconstructed interference signal by performing IFFT calculation on the multiplied signal.
Alternatively, the signal reconstructor, used as part of the larger channel estimator, implements interference reconstruction by multiplying the pre-equalized signal (as described in claim 2) with a channel estimation value from the previous frame. An IFFT circuit then transforms this multiplied signal back into the time domain, generating the reconstructed interference signal.
6. The channel estimator of claim 1 , wherein the updating circuit is further configured to generate an updated channel estimation value by updating the current channel estimation value with the error using least mean squares algorithm.
The updating circuit, which refines the channel estimate based on the error between the estimated and actual training sequence, uses a Least Mean Squares (LMS) algorithm to perform this update. Specifically, the current channel estimation value is updated with the error using LMS.
7. The channel estimator of claim 1 , wherein the channel estimator is used in a single carrier mode, and the channel estimator reuses a decision feedback equalizer.
This channel estimator, which receives a time-domain training sequence symbol, estimates the training sequence using convolution, calculates the error, refines the channel estimate, repeats for each symbol, and then outputs the final estimate, is used in a single carrier mode and reuses a decision feedback equalizer.
8. A demodulator comprising the channel estimator according claim 1 .
A demodulator incorporates the channel estimator described previously, which receives a time-domain training sequence symbol, estimates the training sequence using convolution, calculates the error, refines the channel estimate, repeats for each symbol, and then outputs the final estimate.
9. A method of channel estimation, comprising: receiving, at a receiver, a first time-domain training sequence; generating an estimated value for the first time-domain training sequence by convoluting a second time-domain training sequence with a current channel estimation value, wherein the second time-domain training sequence represents a time-domain training sequence generated by the receiver; generating an error by subtracting the estimated value for the first time-domain training sequence from a value of the first time-domain training sequence; generating an updated channel estimation value by updating the current channel estimation value with the error; and iteratively receiving a next symbol of the first time-domain training sequence, generating an estimated value for an updated first time-domain training sequence, generating an updated error, and generating an update of the updated channel estimation value, until completion of receipt of a last symbol of the first time-domain training sequence, and outputting the current updated channel estimation value as a channel estimation result upon completion of receipt of a last symbol of the first time-domain training sequence.
A method for channel estimation involves receiving a time-domain training sequence. The method estimates the training sequence by convolving another time-domain training sequence (generated by the receiver) with the current channel estimate. The error between the estimated and actual training sequence is calculated. The channel estimate is updated using this error. This process of receiving the next symbol, estimating the training sequence, calculating the error, and updating the estimate is repeated for each symbol of the training sequence. Finally, the fully updated channel estimate is outputted after processing all symbols.
10. The method of claim 9 , further comprising generating a pre-equalized signal by pre-equalizing a received signal; generating a reconstructed interference signal based on the pre-equalized signal; and generating the first time-domain training sequence by subtracting the reconstructed interference signal from a received signal for a frame.
In addition to the channel estimation method described above, this method also includes pre-equalizing a received signal to generate a pre-equalized signal. An estimated interference signal is generated based on the pre-equalized signal. Then, the primary time-domain training sequence is generated by subtracting this reconstructed interference from the originally received signal for a given frame.
11. The method of claim 10 , wherein generating a pre-equalized signal by pre-equalizing a received signal is implemented by generating a FFT result by performing FFT calculation on the received signal; generating a quotient by dividing the FFT result by a channel estimation value of a previous frame; and retrieving a transmitting signal based on the quotient.
The step of pre-equalizing the received signal (as described in claim 10) involves applying an FFT to the received signal. The FFT result is then divided by a channel estimation value from the previous frame. Finally, the transmitted signal is determined based on the resulting quotient.
12. The method of claim 10 , wherein generating a reconstructed interference signal is implemented by: generating an IFFT result by performing IFFT calculation on the pre-equalized signal; and generating the reconstructed interference signal by convoluting the IFFT result with a channel estimation value of a previous frame.
Generating the reconstructed interference signal (as described in claim 10) involves applying an IFFT to the pre-equalized signal. The reconstructed interference signal is then generated by convolving the IFFT result with a channel estimation value from a previous frame.
13. The method of claim 10 , wherein generating a reconstructed interference signal is implemented by: generating a multiplied signal by multiplying the pre-equalized signal with a channel estimation value of a previous frame; and generating the reconstructed interference signal by performing IFFT calculation on the multiplied signal.
Generating the reconstructed interference signal (as described in claim 10) involves multiplying the pre-equalized signal with a channel estimation value from a previous frame. The reconstructed interference signal is then generated by applying an IFFT to this multiplied signal.
14. The method of claim 9 , wherein generating an updated channel estimation value by updating the current channel estimation value with the error is implemented by generating an updated channel estimation value by updating the current channel estimation value with the error using least mean squares algorithm.
The step of updating the channel estimate (as described in claim 9, which involves receiving a time-domain training sequence, estimating the sequence using convolution, calculating the error, iteratively refining the estimate, and outputting the final estimate) uses a Least Mean Squares (LMS) algorithm. Specifically, the current channel estimation value is updated with the error using LMS.
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December 21, 2015
May 30, 2017
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